Instructional support platform for interactive learning environments

Patent metadata

Field	Value
Publication number	US-10692391-B2
Application number	US-201916551889-A
Country	US
Kind code	B2
Filing date	Aug 27, 2019
Priority date	Dec 1, 2015
Publication date	Jun 23, 2020
Grant date	Jun 23, 2020

Abstract

In various embodiments, subject matter for improving discussions in connection with an educational resource is identified and summarized by analyzing annotations made by students assigned to a discussion group to identify high-quality annotations likely to generate responses and stimulate discussion threads, identifying clusters of high-quality annotations relating to the same portion or related portions of the educational resource, extracting and summarizing text from the annotations, and combining, in an electronically represented document, the extracted and summarized text and (i) at least some of the annotations and the portion or portions of the educational resource or (ii) clickable links thereto.

First claim

What is claimed is:

1. A method of improving online discussions in connection with an educational resource provided to students over network-connected devices, the method comprising:
(a) distributing an interactive educational resource over a network to a plurality of student devices, the student devices being associated with students currently enrolled in a class utilizing the educational resource;
(b) hosting, at a discussion server, an online discussion for receiving and making visible, to student devices assigned to a discussion group, annotations concerning the educational resource received by the discussion server from the student devices assigned to the discussion group;
(c) computationally analyzing annotations to identify high-quality annotations likely to generate responses and stimulate discussion threads; and
(d) making the identified annotations visible to student devices associated with students who are not assigned to the discussion group.

2. The method of claim 1 , further comprising, prior to step (c):
receiving an initial set of annotations at the discussion server, each of the initial set of annotations having a discussion thread associated therewith, wherein at least a portion of the initial set of annotations constitutes a training set;
extracting portions of annotations within the training set, thereby producing a plurality of seed features; and
computationally deriving, from the seed features, one or more evaluation features predictive of thread lengths of discussion threads associated with annotations in the training set.

3. The method of claim 2 , wherein step (c) comprises using a machine-learning model to predict a thread length associated with each annotation based on the one or more evaluation features, the model being predictive in accordance with a prediction algorithm and generated by steps comprising:
dividing the initial set of annotations into the training set and a testing set, each of the training set and the testing set comprising a plurality of annotations and thread lengths associated therewith; and
identifying the one or more evaluation features based on predictive reliability in accordance with the prediction algorithm.

4. The method of claim 3 , further comprising:
computationally predicting, based on the one or more evaluation features, thread lengths for one or more annotations within the testing set; and
adjusting parameters of the model based on the predictions prior to computationally analyzing annotations not within the testing set or training set to identify high-quality annotations.

5. The method of claim 3 , wherein the prediction algorithm is a classification tree.

6. The method of claim 5 , wherein the prediction algorithm is a random forest comprising a plurality of regression trees.

7. The method of claim 2 , wherein producing the plurality of seed features comprises applying natural-language processing to annotations within the training set.

8. The method of claim 1 , wherein the discussion server hosts a plurality of simultaneous discussions each visible only to a discussion group consisting of a subset of the students enrolled in the class.

9. The method of claim 8 , wherein the annotations are analyzed within each discussion group and identified annotations within one discussion group are made visible to student devices associated with students who are in the other discussion groups.

10. The method of claim 1 , wherein the discussion group corresponds to a first session of the class and the students who are not in the discussion group are enrolled in a second, different session of the class.

11. The method of claim 1 , further comprising, after step (c):
extracting and summarizing text from one or more high-quality annotations indicative of a topic to which the one or more high-quality annotations relate; and
combining, in an electronically represented document, the extracted and summarized text and/or (i) the one or more high-quality annotations or (ii) clickable links thereto.

12. The method of claim 11 , wherein the text from the one or more high-quality annotations is represented in the document in the form of a panel.

13. The method of claim 1 , further comprising, after step (d), redefining the discussion group to include one or more students not assigned to the discussion group in step (b).

14. An educational system comprising:
a plurality of student devices for executing an interactive educational resource received over a network, the student devices being configured to receive student annotations associated with the educational resource and transmit at least some of the annotations to a discussion server;
a student database;
a resource server in electronic communication with the student devices, the resource server comprising a communication module and being configured to make the resource available to student devices associated with students enrolled in a class;
a discussion server, in electronic communication with the student devices, for receiving and making visible, to student devices assigned to a discussion group in the student database, annotations concerning the educational resource received from the student devices assigned to the discussion group; and
an analysis module for computationally analyzing annotations to identify high-quality annotations likely to generate responses and stimulate discussion threads,
wherein the discussion server is configured to make the identified annotations visible to student devices associated with students who are not assigned to the discussion group.

15. The system of claim 14 , wherein the analysis module is configured to:
extract portions of annotations within a training set of annotations, thereby producing a plurality of seed features; and
computationally derive, from the seed features, one or more evaluation features predictive of thread lengths of discussion threads associated with annotations in the training set.

16. The system of claim 15 , wherein the analysis module uses a machine-learning model to predict a thread length associated with each annotation based on the one or more evaluation features, the model being predictive in accordance with a prediction algorithm and generated by steps comprising:
dividing an initial set of annotations into the training set and a testing set, each of the training set and the testing set comprising a plurality of annotations and thread lengths associated therewith; and
identifying the one or more evaluation features based on predictive reliability in accordance with the prediction algorithm.

17. The system of claim 16 , wherein the analysis module is configured to:
computationally predict, based on the one or more evaluation features, thread lengths for one or more annotations within the testing set; and
adjust parameters of the model based on the predictions.

18. The system of claim 16 , wherein the prediction algorithm is a classification tree.

19. The system of claim 18 , wherein the prediction algorithm is a random forest comprising a plurality of regression trees.

20. The system of claim 15 , wherein the analysis module is configured to produce the plurality of seed features by applying natural-language processing to annotations within the training set.

21. The system of claim 14 , wherein the discussion server hosts a plurality of simultaneous discussions each visible only to a discussion group consisting of a subset of the students enrolled in the class.

22. The system of claim 21 ,

Assignees

• King Gary
• Mazur Eric
• Miller Kelly
• Lukoff Brian
• Harvard College

Inventors

• King Gary
• Mazur Eric
• Miller Kelly
• Lukoff Brian

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F40/169

  Annotation, e.g. comment data or footnotes · CPC title

• G09B5/02 Primary

  with visual presentation of the material to be studied, e.g. using film strip · CPC title